Overview

Dataset statistics

Number of variables18
Number of observations8950
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory144.0 B

Variable types

Categorical1
Numeric17

Alerts

cust_id has a high cardinality: 8950 distinct values High cardinality
balance is highly correlated with balance_frequency and 4 other fieldsHigh correlation
balance_frequency is highly correlated with balanceHigh correlation
purchases is highly correlated with oneoff_purchases and 5 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with balance and 2 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance_frequency is highly correlated with balance and 2 other fieldsHigh correlation
cash_advance_trx is highly correlated with balance and 2 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
minimum_payments is highly correlated with balanceHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 3 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 2 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with oneoff_purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
credit_limit is highly correlated with balanceHigh correlation
payments is highly correlated with purchases and 1 other fieldsHigh correlation
balance is highly correlated with minimum_paymentsHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 1 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
minimum_payments is highly correlated with balanceHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_trx and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 1 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases_frequencyHigh correlation
purchases_installments_frequency is highly correlated with purchases_frequencyHigh correlation
cash_advance_frequency is highly correlated with cash_advance_trxHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
credit_limit is highly correlated with balance and 2 other fieldsHigh correlation
payments is highly correlated with purchases and 4 other fieldsHigh correlation
cust_id is uniformly distributed Uniform
cust_id has unique values Unique
purchases has 2044 (22.8%) zeros Zeros
oneoff_purchases has 4302 (48.1%) zeros Zeros
installments_purchases has 3916 (43.8%) zeros Zeros
cash_advance has 4628 (51.7%) zeros Zeros
purchases_frequency has 2043 (22.8%) zeros Zeros
oneoff_purchases_frequency has 4302 (48.1%) zeros Zeros
purchases_installments_frequency has 3915 (43.7%) zeros Zeros
cash_advance_frequency has 4628 (51.7%) zeros Zeros
cash_advance_trx has 4628 (51.7%) zeros Zeros
purchases_trx has 2044 (22.8%) zeros Zeros
payments has 240 (2.7%) zeros Zeros
prc_full_payment has 5903 (66.0%) zeros Zeros

Reproduction

Analysis started2021-10-31 13:48:46.185118
Analysis finished2021-10-31 13:51:34.912147
Duration2 minutes and 48.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

cust_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
C10001
 
1
C16135
 
1
C16129
 
1
C16130
 
1
C16131
 
1
Other values (8945)
8945 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8950 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10004
5th rowC10005

Common Values

ValueCountFrequency (%)
C100011
 
< 0.1%
C161351
 
< 0.1%
C161291
 
< 0.1%
C161301
 
< 0.1%
C161311
 
< 0.1%
C161321
 
< 0.1%
C161331
 
< 0.1%
C161341
 
< 0.1%
C161361
 
< 0.1%
C161441
 
< 0.1%
Other values (8940)8940
99.9%

Length

2021-10-31T10:51:35.224975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c100011
 
< 0.1%
c100111
 
< 0.1%
c100171
 
< 0.1%
c100161
 
< 0.1%
c100151
 
< 0.1%
c100141
 
< 0.1%
c100131
 
< 0.1%
c100121
 
< 0.1%
c100101
 
< 0.1%
c100341
 
< 0.1%
Other values (8940)8940
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8871
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.474828
Minimum0
Maximum19043.13856
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:35.679722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.81451835
Q1128.2819155
median873.385231
Q32054.140036
95-th percentile5909.111808
Maximum19043.13856
Range19043.13856
Interquartile range (IQR)1925.85812

Descriptive statistics

Standard deviation2081.531879
Coefficient of variation (CV)1.330498799
Kurtosis7.6747513
Mean1564.474828
Median Absolute Deviation (MAD)799.865197
Skewness2.393386043
Sum14002049.71
Variance4332774.965
MonotonicityNot monotonic
2021-10-31T10:51:36.123477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
0.9%
40.9007491
 
< 0.1%
1213.5513381
 
< 0.1%
1253.1883171
 
< 0.1%
5058.2996351
 
< 0.1%
296.9059441
 
< 0.1%
1084.6526471
 
< 0.1%
237.1984421
 
< 0.1%
1636.5183151
 
< 0.1%
468.8514151
 
< 0.1%
Other values (8861)8861
99.0%
ValueCountFrequency (%)
080
0.9%
0.0001991
 
< 0.1%
0.0011461
 
< 0.1%
0.0012141
 
< 0.1%
0.0012891
 
< 0.1%
0.0048161
 
< 0.1%
0.0066511
 
< 0.1%
0.0096841
 
< 0.1%
0.019681
 
< 0.1%
0.0211021
 
< 0.1%
ValueCountFrequency (%)
19043.138561
< 0.1%
18495.558551
< 0.1%
16304.889251
< 0.1%
16259.448571
< 0.1%
16115.59641
< 0.1%
15532.339721
< 0.1%
15258.22591
< 0.1%
15244.748651
< 0.1%
15155.532861
< 0.1%
14581.459141
< 0.1%

balance_frequency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8772707256
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:36.727144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.2369040027
Coefficient of variation (CV)0.2700466296
Kurtosis3.092369622
Mean0.8772707256
Median Absolute Deviation (MAD)0
Skewness-2.023265519
Sum7851.572994
Variance0.05612350649
MonotonicityNot monotonic
2021-10-31T10:51:37.279840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16211
69.4%
0.909091410
 
4.6%
0.818182278
 
3.1%
0.727273223
 
2.5%
0.545455219
 
2.4%
0.636364209
 
2.3%
0.454545172
 
1.9%
0.363636170
 
1.9%
0.272727151
 
1.7%
0.181818146
 
1.6%
Other values (33)761
 
8.5%
ValueCountFrequency (%)
080
0.9%
0.09090967
0.7%
0.18
 
0.1%
0.1111115
 
0.1%
0.1259
 
0.1%
0.1428577
 
0.1%
0.1666677
 
0.1%
0.181818146
1.6%
0.29
 
0.1%
0.2222225
 
0.1%
ValueCountFrequency (%)
16211
69.4%
0.909091410
 
4.6%
0.955
 
0.6%
0.88888953
 
0.6%
0.87557
 
0.6%
0.85714351
 
0.6%
0.83333360
 
0.7%
0.818182278
 
3.1%
0.820
 
0.2%
0.77777822
 
0.2%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.204834
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:37.857522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.635
median361.28
Q31110.13
95-th percentile3998.6195
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.495

Descriptive statistics

Standard deviation2136.634782
Coefficient of variation (CV)2.129809098
Kurtosis111.3887709
Mean1003.204834
Median Absolute Deviation (MAD)361.28
Skewness8.144269065
Sum8978683.26
Variance4565208.191
MonotonicityNot monotonic
2021-10-31T10:51:38.428208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
 
22.8%
45.6527
 
0.3%
6016
 
0.2%
15016
 
0.2%
30013
 
0.1%
20013
 
0.1%
10013
 
0.1%
45012
 
0.1%
5010
 
0.1%
60010
 
0.1%
Other values (6193)6776
75.7%
ValueCountFrequency (%)
02044
22.8%
0.014
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
0.71
 
< 0.1%
12
 
< 0.1%
1.41
 
< 0.1%
21
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
ValueCountFrequency (%)
49039.571
< 0.1%
41050.41
< 0.1%
40040.711
< 0.1%
38902.711
< 0.1%
35131.161
< 0.1%
32539.781
< 0.1%
31299.351
< 0.1%
27957.681
< 0.1%
27790.421
< 0.1%
26784.621
< 0.1%

oneoff_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4014
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.4373709
Minimum0
Maximum40761.25
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:39.023879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.405
95-th percentile2671.094
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.405

Descriptive statistics

Standard deviation1659.887917
Coefficient of variation (CV)2.801794753
Kurtosis164.187572
Mean592.4373709
Median Absolute Deviation (MAD)38
Skewness10.04508288
Sum5302314.47
Variance2755227.898
MonotonicityNot monotonic
2021-10-31T10:51:39.621551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04302
48.1%
45.6546
 
0.5%
5017
 
0.2%
20015
 
0.2%
6013
 
0.1%
10013
 
0.1%
15012
 
0.1%
7012
 
0.1%
100012
 
0.1%
25011
 
0.1%
Other values (4004)4497
50.2%
ValueCountFrequency (%)
04302
48.1%
0.017
 
0.1%
0.022
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
0.71
 
< 0.1%
14
 
< 0.1%
1.42
 
< 0.1%
21
 
< 0.1%
4.991
 
< 0.1%
ValueCountFrequency (%)
40761.251
< 0.1%
40624.061
< 0.1%
34087.731
< 0.1%
33803.841
< 0.1%
26547.431
< 0.1%
26514.321
< 0.1%
25122.771
< 0.1%
24543.521
< 0.1%
23032.971
< 0.1%
22257.391
< 0.1%

installments_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.0676447
Minimum0
Maximum22500
Zeros3916
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:40.195233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.6375
95-th percentile1750.0875
Maximum22500
Range22500
Interquartile range (IQR)468.6375

Descriptive statistics

Standard deviation904.3381152
Coefficient of variation (CV)2.199973963
Kurtosis96.57517753
Mean411.0676447
Median Absolute Deviation (MAD)89
Skewness7.299119909
Sum3679055.42
Variance817827.4266
MonotonicityNot monotonic
2021-10-31T10:51:40.773913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03916
43.8%
30014
 
0.2%
20014
 
0.2%
10014
 
0.2%
15012
 
0.1%
12511
 
0.1%
759
 
0.1%
3508
 
0.1%
4508
 
0.1%
5008
 
0.1%
Other values (4442)4936
55.2%
ValueCountFrequency (%)
03916
43.8%
1.951
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
6.331
 
< 0.1%
7.261
 
< 0.1%
7.671
 
< 0.1%
9.281
 
< 0.1%
9.581
 
< 0.1%
9.651
 
< 0.1%
ValueCountFrequency (%)
225001
< 0.1%
15497.191
< 0.1%
14686.11
< 0.1%
13184.431
< 0.1%
12738.471
< 0.1%
12560.851
< 0.1%
125411
< 0.1%
123751
< 0.1%
12235.051
< 0.1%
12128.941
< 0.1%

cash_advance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.8711125
Minimum0
Maximum47137.21176
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:41.177690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.821139
95-th percentile4647.169122
Maximum47137.21176
Range47137.21176
Interquartile range (IQR)1113.821139

Descriptive statistics

Standard deviation2097.163877
Coefficient of variation (CV)2.142431062
Kurtosis52.89943411
Mean978.8711125
Median Absolute Deviation (MAD)0
Skewness5.166609074
Sum8760896.457
Variance4398096.325
MonotonicityNot monotonic
2021-10-31T10:51:41.844324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
495.4258321
 
< 0.1%
1486.2432931
 
< 0.1%
855.2327791
 
< 0.1%
3767.1047071
 
< 0.1%
291.6085121
 
< 0.1%
38.6905521
 
< 0.1%
521.6643691
 
< 0.1%
1974.2029631
 
< 0.1%
2462.1007891
 
< 0.1%
Other values (4313)4313
48.2%
ValueCountFrequency (%)
04628
51.7%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.2045771
 
< 0.1%
18.2406261
 
< 0.1%
18.2800431
 
< 0.1%
ValueCountFrequency (%)
47137.211761
< 0.1%
29282.109151
< 0.1%
27296.485761
< 0.1%
26268.699891
< 0.1%
26194.049541
< 0.1%
23130.821061
< 0.1%
22665.77851
< 0.1%
21943.849421
< 0.1%
20712.670081
< 0.1%
20277.331121
< 0.1%

purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4903505484
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:42.436996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.4013707474
Coefficient of variation (CV)0.8185383879
Kurtosis-1.638630948
Mean0.4903505484
Median Absolute Deviation (MAD)0.416667
Skewness0.06016423586
Sum4388.637408
Variance0.1610984768
MonotonicityNot monotonic
2021-10-31T10:51:42.927725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
12178
24.3%
02043
22.8%
0.083333677
 
7.6%
0.916667396
 
4.4%
0.5395
 
4.4%
0.166667392
 
4.4%
0.833333373
 
4.2%
0.333333367
 
4.1%
0.25345
 
3.9%
0.583333316
 
3.5%
Other values (37)1468
16.4%
ValueCountFrequency (%)
02043
22.8%
0.083333677
 
7.6%
0.09090943
 
0.5%
0.127
 
0.3%
0.11111118
 
0.2%
0.12532
 
0.4%
0.14285726
 
0.3%
0.166667392
 
4.4%
0.18181816
 
0.2%
0.219
 
0.2%
ValueCountFrequency (%)
12178
24.3%
0.916667396
 
4.4%
0.90909128
 
0.3%
0.924
 
0.3%
0.88888918
 
0.2%
0.87526
 
0.3%
0.85714325
 
0.3%
0.833333373
 
4.2%
0.81818221
 
0.2%
0.89
 
0.1%

oneoff_purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2024576836
Minimum0
Maximum1
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:43.443441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2983360652
Coefficient of variation (CV)1.473572452
Kurtosis1.161845601
Mean0.2024576836
Median Absolute Deviation (MAD)0.083333
Skewness1.535612784
Sum1811.996268
Variance0.08900440779
MonotonicityNot monotonic
2021-10-31T10:51:44.102079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04302
48.1%
0.0833331104
 
12.3%
0.166667592
 
6.6%
1481
 
5.4%
0.25418
 
4.7%
0.333333355
 
4.0%
0.416667244
 
2.7%
0.5235
 
2.6%
0.583333197
 
2.2%
0.666667167
 
1.9%
Other values (37)855
 
9.6%
ValueCountFrequency (%)
04302
48.1%
0.0833331104
 
12.3%
0.09090956
 
0.6%
0.139
 
0.4%
0.11111126
 
0.3%
0.12541
 
0.5%
0.14285737
 
0.4%
0.166667592
 
6.6%
0.18181834
 
0.4%
0.227
 
0.3%
ValueCountFrequency (%)
1481
5.4%
0.916667151
 
1.7%
0.9090914
 
< 0.1%
0.91
 
< 0.1%
0.8888892
 
< 0.1%
0.8756
 
0.1%
0.8571431
 
< 0.1%
0.833333120
 
1.3%
0.81818210
 
0.1%
0.84
 
< 0.1%

purchases_installments_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3644373416
Minimum0
Maximum1
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:44.747722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.3974477797
Coefficient of variation (CV)1.090579187
Kurtosis-1.398632185
Mean0.3644373416
Median Absolute Deviation (MAD)0.166667
Skewness0.509201165
Sum3261.714207
Variance0.1579647376
MonotonicityNot monotonic
2021-10-31T10:51:45.375376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
03915
43.7%
11331
 
14.9%
0.416667388
 
4.3%
0.916667345
 
3.9%
0.833333311
 
3.5%
0.5310
 
3.5%
0.166667305
 
3.4%
0.666667292
 
3.3%
0.75291
 
3.3%
0.083333275
 
3.1%
Other values (37)1187
 
13.3%
ValueCountFrequency (%)
03915
43.7%
0.083333275
 
3.1%
0.09090912
 
0.1%
0.16
 
0.1%
0.1111119
 
0.1%
0.1255
 
0.1%
0.1428576
 
0.1%
0.166667305
 
3.4%
0.18181814
 
0.2%
0.29
 
0.1%
ValueCountFrequency (%)
11331
14.9%
0.916667345
 
3.9%
0.90909125
 
0.3%
0.919
 
0.2%
0.88888928
 
0.3%
0.87528
 
0.3%
0.85714330
 
0.3%
0.833333311
 
3.5%
0.81818221
 
0.2%
0.818
 
0.2%

cash_advance_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351442003
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:45.905084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.2001213881
Coefficient of variation (CV)1.480798937
Kurtosis3.334734328
Mean0.1351442003
Median Absolute Deviation (MAD)0
Skewness1.828686266
Sum1209.540593
Variance0.04004856999
MonotonicityNot monotonic
2021-10-31T10:51:46.454780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
0.0833331021
 
11.4%
0.166667759
 
8.5%
0.25578
 
6.5%
0.333333439
 
4.9%
0.416667273
 
3.1%
0.5215
 
2.4%
0.583333142
 
1.6%
0.666667125
 
1.4%
0.09090970
 
0.8%
Other values (44)700
 
7.8%
ValueCountFrequency (%)
04628
51.7%
0.0833331021
 
11.4%
0.09090970
 
0.8%
0.139
 
0.4%
0.11111129
 
0.3%
0.12547
 
0.5%
0.14285749
 
0.5%
0.166667759
 
8.5%
0.18181842
 
0.5%
0.221
 
0.2%
ValueCountFrequency (%)
1.51
 
< 0.1%
1.251
 
< 0.1%
1.1666672
 
< 0.1%
1.1428571
 
< 0.1%
1.1251
 
< 0.1%
1.11
 
< 0.1%
1.0909091
 
< 0.1%
125
0.3%
0.91666727
0.3%
0.9090913
 
< 0.1%

cash_advance_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.248826816
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:47.088432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.824646744
Coefficient of variation (CV)2.100649598
Kurtosis61.64686248
Mean3.248826816
Median Absolute Deviation (MAD)0
Skewness5.721298203
Sum29077
Variance46.57580318
MonotonicityNot monotonic
2021-10-31T10:51:47.628134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
10150
 
1.7%
Other values (55)915
 
10.2%
ValueCountFrequency (%)
04628
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
9111
 
1.2%
ValueCountFrequency (%)
1233
< 0.1%
1101
 
< 0.1%
1071
 
< 0.1%
931
 
< 0.1%
801
 
< 0.1%
711
 
< 0.1%
691
 
< 0.1%
631
 
< 0.1%
623
< 0.1%
611
 
< 0.1%

purchases_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.7098324
Minimum0
Maximum358
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:48.282774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.85764911
Coefficient of variation (CV)1.689866236
Kurtosis34.79310026
Mean14.7098324
Median Absolute Deviation (MAD)7
Skewness4.630655266
Sum131653
Variance617.9027193
MonotonicityNot monotonic
2021-10-31T10:51:48.960398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
22.8%
1667
 
7.5%
12570
 
6.4%
2379
 
4.2%
6352
 
3.9%
3314
 
3.5%
4285
 
3.2%
7275
 
3.1%
5267
 
3.0%
8267
 
3.0%
Other values (163)3530
39.4%
ValueCountFrequency (%)
02044
22.8%
1667
 
7.5%
2379
 
4.2%
3314
 
3.5%
4285
 
3.2%
5267
 
3.0%
6352
 
3.9%
7275
 
3.1%
8267
 
3.0%
9248
 
2.8%
ValueCountFrequency (%)
3581
< 0.1%
3471
< 0.1%
3441
< 0.1%
3091
< 0.1%
3081
< 0.1%
2981
< 0.1%
2741
< 0.1%
2731
< 0.1%
2541
< 0.1%
2482
< 0.1%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct206
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4494.44945
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:49.638025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.612411
Coefficient of variation (CV)0.8095791156
Kurtosis2.837308005
Mean4494.44945
Median Absolute Deviation (MAD)1800
Skewness1.522549038
Sum40225322.58
Variance13239500.28
MonotonicityNot monotonic
2021-10-31T10:51:50.286669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000784
 
8.8%
1500722
 
8.1%
1200621
 
6.9%
1000614
 
6.9%
2500612
 
6.8%
4000506
 
5.7%
6000463
 
5.2%
5000389
 
4.3%
2000371
 
4.1%
7500277
 
3.1%
Other values (196)3591
40.1%
ValueCountFrequency (%)
501
 
< 0.1%
1505
 
0.1%
2003
 
< 0.1%
30014
 
0.2%
4003
 
< 0.1%
4506
 
0.1%
500121
1.4%
60021
 
0.2%
6501
 
< 0.1%
70020
 
0.2%
ValueCountFrequency (%)
300002
 
< 0.1%
280001
 
< 0.1%
250001
 
< 0.1%
230002
 
< 0.1%
225001
 
< 0.1%
220001
 
< 0.1%
215002
 
< 0.1%
210002
 
< 0.1%
205001
 
< 0.1%
2000010
0.1%

payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8711
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.143852
Minimum0
Maximum50721.48336
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:50.931314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.98892395
Q1383.276166
median856.901546
Q31901.134317
95-th percentile6082.090595
Maximum50721.48336
Range50721.48336
Interquartile range (IQR)1517.858151

Descriptive statistics

Standard deviation2895.063757
Coefficient of variation (CV)1.670411693
Kurtosis54.77073581
Mean1733.143852
Median Absolute Deviation (MAD)581.3514605
Skewness5.907619794
Sum15511637.48
Variance8381394.157
MonotonicityNot monotonic
2021-10-31T10:51:51.695889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0240
 
2.7%
201.8020841
 
< 0.1%
398.3164411
 
< 0.1%
826.0367481
 
< 0.1%
2571.5732141
 
< 0.1%
1903.2796431
 
< 0.1%
454.8885061
 
< 0.1%
956.0287471
 
< 0.1%
4560.775721
 
< 0.1%
1825.3499551
 
< 0.1%
Other values (8701)8701
97.2%
ValueCountFrequency (%)
0240
2.7%
0.0495131
 
< 0.1%
0.0564661
 
< 0.1%
2.3895831
 
< 0.1%
3.5005051
 
< 0.1%
4.5235551
 
< 0.1%
4.8415431
 
< 0.1%
5.0707261
 
< 0.1%
9.0400171
 
< 0.1%
9.5333131
 
< 0.1%
ValueCountFrequency (%)
50721.483361
< 0.1%
46930.598241
< 0.1%
40627.595241
< 0.1%
39461.96581
< 0.1%
39048.597621
< 0.1%
36066.750681
< 0.1%
35843.625931
< 0.1%
34107.074991
< 0.1%
33994.727851
< 0.1%
33486.310441
< 0.1%

minimum_payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct8637
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864.2065423
Minimum0.019163
Maximum76406.20752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:53.157084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile74.6441173
Q1170.8576542
median335.6283125
Q3864.2065423
95-th percentile2719.566935
Maximum76406.20752
Range76406.18836
Interquartile range (IQR)693.3488881

Descriptive statistics

Standard deviation2330.588021
Coefficient of variation (CV)2.696795161
Kurtosis294.3843928
Mean864.2065423
Median Absolute Deviation (MAD)211.02137
Skewness13.86735726
Sum7734648.554
Variance5431640.525
MonotonicityNot monotonic
2021-10-31T10:51:53.744759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864.2065423313
 
3.5%
299.3518812
 
< 0.1%
150.3171431
 
< 0.1%
271.5281691
 
< 0.1%
6404.8554841
 
< 0.1%
616.8625441
 
< 0.1%
211.9841931
 
< 0.1%
324.9547471
 
< 0.1%
1600.269171
 
< 0.1%
277.5467131
 
< 0.1%
Other values (8627)8627
96.4%
ValueCountFrequency (%)
0.0191631
< 0.1%
0.0377441
< 0.1%
0.055881
< 0.1%
0.0594811
< 0.1%
0.1170361
< 0.1%
0.2619841
< 0.1%
0.3119531
< 0.1%
0.3194751
< 0.1%
1.1130271
< 0.1%
1.3340751
< 0.1%
ValueCountFrequency (%)
76406.207521
< 0.1%
61031.61861
< 0.1%
56370.041171
< 0.1%
50260.759471
< 0.1%
43132.728231
< 0.1%
42629.551171
< 0.1%
38512.124771
< 0.1%
31871.363791
< 0.1%
30528.43241
< 0.1%
29019.802881
< 0.1%

prc_full_payment
Real number (ℝ≥0)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1537146485
Minimum0
Maximum1
Zeros5903
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:54.365417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2924991962
Coefficient of variation (CV)1.902871321
Kurtosis2.432395301
Mean0.1537146485
Median Absolute Deviation (MAD)0
Skewness1.942819941
Sum1375.746104
Variance0.0855557798
MonotonicityNot monotonic
2021-10-31T10:51:54.820168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
05903
66.0%
1488
 
5.5%
0.083333426
 
4.8%
0.166667166
 
1.9%
0.5156
 
1.7%
0.25156
 
1.7%
0.090909153
 
1.7%
0.333333134
 
1.5%
0.194
 
1.1%
0.283
 
0.9%
Other values (37)1191
 
13.3%
ValueCountFrequency (%)
05903
66.0%
0.083333426
 
4.8%
0.090909153
 
1.7%
0.194
 
1.1%
0.11111161
 
0.7%
0.12552
 
0.6%
0.14285754
 
0.6%
0.166667166
 
1.9%
0.18181875
 
0.8%
0.283
 
0.9%
ValueCountFrequency (%)
1488
5.5%
0.91666777
 
0.9%
0.90909119
 
0.2%
0.916
 
0.2%
0.88888912
 
0.1%
0.87518
 
0.2%
0.85714312
 
0.1%
0.83333363
 
0.7%
0.81818217
 
0.2%
0.833
 
0.4%

tenure
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.51731844
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-10-31T10:51:55.293906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.338330769
Coefficient of variation (CV)0.1162015947
Kurtosis7.694823186
Mean11.51731844
Median Absolute Deviation (MAD)0
Skewness-2.943017288
Sum103080
Variance1.791129248
MonotonicityNot monotonic
2021-10-31T10:51:55.830609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127584
84.7%
11365
 
4.1%
10236
 
2.6%
6204
 
2.3%
8196
 
2.2%
7190
 
2.1%
9175
 
2.0%
ValueCountFrequency (%)
6204
 
2.3%
7190
 
2.1%
8196
 
2.2%
9175
 
2.0%
10236
 
2.6%
11365
 
4.1%
127584
84.7%
ValueCountFrequency (%)
127584
84.7%
11365
 
4.1%
10236
 
2.6%
9175
 
2.0%
8196
 
2.2%
7190
 
2.1%
6204
 
2.3%

Interactions

2021-10-31T10:51:23.653355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:48:55.784825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:02.971861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:12.954355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:22.988900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:33.731975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:44.312141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:53.437108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:02.554080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:13.235191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-10-31T10:49:20.767766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:30.720636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:41.365766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:50.719608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:59.485773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:10.185872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:21.405686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:29.804053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:38.575215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:48.127948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:55.936641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:04.151110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:12.469523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:21.573503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:30.290696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:00.830041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:09.977997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:21.244505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:31.272332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:41.950443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:51.188348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:59.945518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:10.658613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:21.999356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:30.291783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:39.092929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:48.498743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:56.490335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:04.656832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:12.992236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:21.892329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:30.882370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:01.363748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:10.661620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:21.641452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:31.745073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:42.505138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:51.745043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:00.410263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:11.347231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:22.440113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:30.757527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:39.439738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:48.944497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:56.978067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:05.247505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:13.383018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:22.256126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:31.451054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:01.939430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:11.373228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:22.067562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:32.223809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:43.187761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:52.397682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:01.202827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:11.964893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:23.060771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:31.307224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:39.926469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:49.568152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:57.425819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:05.687263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:13.889739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:22.628921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:31.868825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:02.439154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:12.055852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:22.523346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:33.078338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:43.739458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:49:52.970369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:01.840475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:12.639520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:23.625461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:31.743984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:40.345240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:49.966933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:50:57.863578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:06.170998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:14.255538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-31T10:51:23.145637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-10-31T10:51:56.410291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-31T10:51:57.308794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-31T10:51:58.413186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-31T10:51:59.296699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-31T10:51:32.920244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-31T10:51:34.269502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

cust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenure
0C1000140.900.8295.400.0095.400.000.170.000.080.00021000.00201.80139.510.0012
1C100023202.470.910.000.000.006442.950.000.000.000.25407000.004103.031072.340.2212
2C100032495.151.00773.17773.170.000.001.001.000.000.000127500.00622.07627.280.0012
3C100041666.670.641499.001499.000.00205.790.080.080.000.08117500.000.00864.210.0012
4C10005817.711.0016.0016.000.000.000.080.080.000.00011200.00678.33244.790.0012
5C100061809.831.001333.280.001333.280.000.670.000.580.00081800.001400.062407.250.0012
6C10007627.261.007091.016402.63688.380.001.001.001.000.0006413500.006354.31198.071.0012
7C100081823.651.00436.200.00436.200.001.000.001.000.000122300.00679.07532.030.0012
8C100091014.931.00861.49661.49200.000.000.330.080.250.00057000.00688.28311.960.0012
9C10010152.230.551281.601281.600.000.000.170.170.000.000311000.001164.77100.300.0012

Last rows

cust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenure
8940C19181130.841.00591.240.00591.240.001.000.000.830.00061000.00475.5282.771.006
8941C191825967.480.83214.550.00214.558555.410.830.000.670.671359000.00966.20861.950.006
8942C1918340.831.00113.280.00113.280.001.000.000.830.00061000.0094.4986.280.256
8943C191845.870.5020.9020.900.000.000.170.170.000.0001500.0058.6443.470.006
8944C19185193.570.831012.731012.730.000.000.330.330.000.00024000.000.00864.210.006
8945C1918628.491.00291.120.00291.120.001.000.000.830.00061000.00325.5948.890.506
8946C1918719.181.00300.000.00300.000.001.000.000.830.00061000.00275.86864.210.006
8947C1918823.400.83144.400.00144.400.000.830.000.670.00051000.0081.2782.420.256
8948C1918913.460.830.000.000.0036.560.000.000.000.1720500.0052.5555.760.256
8949C19190372.710.671093.251093.250.00127.040.670.670.000.332231200.0063.1788.290.006